How to (Not) Spend Big Data Dollars
A recent Gartner survey estimated that Big Data will spur $232 billion of spending through 2016.
Whether you believe the number to be accurate, there is no denying the fact that Big Data is drawing a lot of attention, and therefore the need for the required budgets will follow. It is simply how our industry works. But the question needs to be asked, how will the attention and money be applied in your business?
International Data Corporation (IDC) outlined their view on how this money will be spent. The spending areas include services, training, manpower, and infrastructure.
I recently discussed what could be a sensible approach for your organization to take in adapting your Big Data initiatives, but you cannot afford to ignore the competitive advantage this data can provide. By doing that, you are putting your company in danger of falling behind the competition.
So spending in this area needs to happen—it is foolhardy not to do it—and you are faced with some decisions. Where to spend is only part of the equation. Unless you have a war chest of excess money lying around, you will also need to consider from where this money will be diverted. This is normally by cutting or curbing the scope on existing projects or legacy systems.
Something else to consider when developing these budgets—there is currently a well-known shortage of people with the needed experience. This implies that a lot of the manpower may need to come from within. This is not all bad, because using in-house talent can be advantageous. Your employees have the domain knowledge and an understanding of the relationships, underlying business use cases, and hidden problems in your data.
The talent will come. In the computer industry we are very good at adapting and feeding new trends, but this takes time. The costs need to be considered for (re)training and ramp-up time.
We love to be part of the new thing, so finding people internally who want to be involved in these projects will not be the issue. But identifying the right people and giving them the tools, budget, and time to adapt is a challenge that must be met head-on.
Traditional Business Intelligence (BI) knowledge is valuable but is usually based on more structured data and relational data models. Here we are talking about larger amounts of data, much of it unstructured and delivered in an unforeseen pace. So there is a learning curve with or without data analysis experience.
Ultimately, time, a little patience, expected hiccups, and realistic budget expectations combined with strictly defined goals are the key to manage and move your Big Data initiatives forward.
How are you planning on financing your Big Data projects?
